library(readxl)
Belgium <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Belgium.xlsx")
View(Belgium)
# Decomposing
BELGIUM_ts_decomp <- decompose(BELGIUM_ts)
# Importing Data
BELGIUM <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Belgium.xlsx")
# Creating Time Series Data
BELGIUM_ts <- ts(BELGIUM, start=c(2004,1), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
BELGIUM_ts
sum(is.na(BELGIUM_ts))
library(forecast)
BELGIUM_ts <- tsclean(BELGIUM_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(BELGIUM_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
BELGIUM_ts_model <- auto.arima(BELGIUM_ts)
BELGIUM_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
BELGIUM_ts_forecast <- forecast (BELGIUM_ts_model, level=c(95), h=258)
plot(BELGIUM_ts_forecast)
BELGIUM_ts_forecast
write.table(BELGIUM_ts_forecast, file="Belgium_TSA.csv", sep=",")
# Importing Data
CANADA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Canada.xlsx")
# Creating Time Series Data
CANADA_ts <- ts(CANADA, start=c(2007,1), end=c(2019,03), frequency=12)
# Viewing and Checking the Created Time Series Data
CANADA_ts
sum(is.na(CANADA_ts))
library(forecast)
CANADA_ts <- tsclean(CANADA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(CANADA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
CANADA_ts_model <- auto.arima(CANADA_ts)
CANADA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
CANADA_ts_forecast <- forecast (CANADA_ts_model, level=c(95), h=261)
plot(CANADA_ts_forecast)
CANADA_ts_forecast
write.table(CANADA_ts_forecast, file="Canada_TSA.csv", sep=",")
# Importing Data
GERMANY <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Germany.xlsx")
# Creating Time Series Data
GERMANY_ts <- ts(GERMANY, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
GERMANY_ts
sum(is.na(GERMANY_ts))
library(forecast)
GERMANY_ts <- tsclean(GERMANY_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(GERMANY_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
GERMANY_ts_model <- auto.arima(GERMANY_ts)
GERMANY_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
GERMANY_ts_forecast <- forecast (GERMANY_ts_model, level=c(95), h=257)
plot(GERMANY_ts_forecast)
GERMANY_ts_forecast
write.table(GERMANY_ts_forecast, file="Germany_TSA.csv", sep=",")
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=257)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
write.table(FRANCE_ts_forecast, file="France_TSA.csv", sep=",")
# Importing Data
THAILAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Thailand.xlsx")
# Creating Time Series Data
THAILAND_ts <- ts(THAILAND, start=c(2007,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
THAILAND_ts
sum(is.na(THAILAND_ts))
library(forecast)
THAILAND_ts <- tsclean(THAILAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(THAILAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
THAILAND_ts_model <- auto.arima(THAILAND_ts)
THAILAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
THAILAND_ts_forecast <- forecast (THAILAND_ts_model, level=c(95), h=257)
plot(THAILAND_ts_forecast)
THAILAND_ts_forecast
write.table(THAILAND_ts_forecast, file="Thailand_TSA.csv", sep=",")
# Importing Data
JAPAN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Japan.xlsx")
# Creating Time Series Data
JAPAN_ts <- ts(JAPAN, start=c(2001,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
JAPAN_ts
sum(is.na(JAPAN_ts))
library(forecast)
JAPAN_ts <- tsclean(JAPAN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(JAPAN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
JAPAN_ts_model <- auto.arima(JAPAN_ts)
JAPAN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
JAPAN_ts_forecast <- forecast (JAPAN_ts_model, level=c(95), h=258)
plot(JAPAN_ts_forecast)
JAPAN_ts_forecast
write.table(JAPAN_ts_forecast, file="Japan_TSA.csv", sep=",")
# Importing Data
NEITHERLAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Neitherland.xlsx")
# Creating Time Series Data
NEITHERLAND_ts <- ts(NEITHERLAND, start=c(2004,03), end=c(2019,04), frequency=12)
# Viewing and Checking the Created Time Series Data
NEITHERLAND_ts
sum(is.na(NEITHERLAND_ts))
library(forecast)
NEITHERLAND_ts <- tsclean(NEITHERLAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(NEITHERLAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
NEITHERLAND_ts_model <- auto.arima(NEITHERLAND_ts)
NEITHERLAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
NEITHERLAND_ts_forecast <- forecast (NEITHERLAND_ts_model, level=c(95), h=260)
plot(NEITHERLAND_ts_forecast)
NEITHERLAND_ts_forecast
write.table(NEITHERLAND_ts_forecast, file="Neitherland_TSA.csv", sep=",")
# Importing Data
USA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/USA.xlsx")
# Creating Time Series Data
USA_ts <- ts(USA, start=c(2005,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
USA_ts
sum(is.na(USA_ts))
library(forecast)
USA_ts <- tsclean(USA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(USA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
USA_ts_model <- auto.arima(USA_ts)
USA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
USA_ts_forecast <- forecast (USA_ts_model, level=c(95), h=257)
plot(USA_ts_forecast)
USA_ts_forecast
write.table(USA_ts_forecast, file="USA_TSA.csv", sep=",")
# Importing Data
KUWAIT <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/KUWAIT.xlsx")
# Creating Time Series Data
KUWAIT_ts <- ts(KUWAIT, start=c(2008,01), end=c(2018,12), frequency=12)
# Viewing and Checking the Created Time Series Data
KUWAIT_ts
sum(is.na(KUWAIT_ts))
library(forecast)
KUWAIT_ts <- tsclean(KUWAIT_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(KUWAIT_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
KUWAIT_ts_model <- auto.arima(KUWAIT_ts)
KUWAIT_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
KUWAIT_ts_forecast <- forecast (KUWAIT_ts_model, level=c(95), h=264)
plot(KUWAIT_ts_forecast)
KUWAIT_ts_forecast
write.table(KUWAIT_ts_forecast, file="Kuwait_TSA.csv", sep=",")
# Importing Data
KOREA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Korea.xlsx")
# Creating Time Series Data
KOREA_ts <- ts(KOREA, start=c(2008,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
KOREA_ts
sum(is.na(KOREA_ts))
library(forecast)
KOREA_ts <- tsclean(KOREA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(KOREA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
KOREA_ts_model <- auto.arima(KOREA_ts)
KOREA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
KOREA_ts_forecast <- forecast (KOREA_ts_model, level=c(95), h=257)
plot(KOREA_ts_forecast)
KOREA_ts_forecast
write.table(KOREA_ts_forecast, file="Korea_TSA.csv", sep=",")
# Importing Data
UK <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/UK.xlsx")
# Creating Time Series Data
UK_ts <- ts(UK, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
UK_ts
sum(is.na(UK_ts))
library(forecast)
UK_ts <- tsclean(UK_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(UK_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
UK_ts_model <- auto.arima(UK_ts)
UK_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
UK_ts_forecast <- forecast (UK_ts_model, level=c(95), h=257)
plot(UK_ts_forecast)
UK_ts_forecast
write.table(UK_ts_forecast, file="UK_TSA.csv", sep=",")
library(readxl)
Kuwait <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/Kuwait.xlsx")
View(Kuwait)
# Importing Data
KUWAIT <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/KUWAIT.xlsx")
# Creating Time Series Data
KUWAIT_ts <- ts(KUWAIT, start=c(2008,01), end=c(2018,12), frequency=12)
# Viewing and Checking the Created Time Series Data
KUWAIT_ts
sum(is.na(KUWAIT_ts))
library(forecast)
KUWAIT_ts <- tsclean(KUWAIT_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(KUWAIT_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
KUWAIT_ts_model <- auto.arima(KUWAIT_ts)
KUWAIT_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
KUWAIT_ts_forecast <- forecast (KUWAIT_ts_model, level=c(95), h=264)
plot(KUWAIT_ts_forecast)
# Importing Data
KUWAIT <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Jordan/KUWAIT.xlsx")
# Creating Time Series Data
KUWAIT_ts <- ts(KUWAIT, start=c(2008,01), end=c(2018,11), frequency=12)
# Viewing and Checking the Created Time Series Data
KUWAIT_ts
sum(is.na(KUWAIT_ts))
library(forecast)
KUWAIT_ts <- tsclean(KUWAIT_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(KUWAIT_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
KUWAIT_ts_model <- auto.arima(KUWAIT_ts)
KUWAIT_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
KUWAIT_ts_forecast <- forecast (KUWAIT_ts_model, level=c(95), h=265)
plot(KUWAIT_ts_forecast)
KUWAIT_ts_forecast
